The invention refers to a method and to an apparatus for encoding eye movement and eye tracking data. In particular the invention refers to the encoding of eye movement and eye tracking data for gaze contingent control within a man machine interaction. The gaze contingency by means of the present invention shall be applied for example to the field of user-dependent eye movement controlled processes within technical apparatus such as machines, devices, computers, equipments, vehicles etc.
From the prior art there are known methods and apparatus for capturing eye movement and eye tracking data from persons in order to display the so-called raw data scanpath, i.e. the time course of gaze, representing the movement of the eyes or visual behavior of the person when viewing pictorial presentations, e.g. photos, advertisement posters or a dynamic scene, e.g. a situation while driving a car, watching a film etc. Usually, eye movement and eye tracking data are captured by using an eye tracking device, a so-called eye-tracker.
Such a method and such an eye tracking device is disclosed e.g. in WO 99/18842 A1. There is described a data evaluation by which typical eye movement patterns can be recognized.
Another method from the prior art known is disclosed e.g. in U.S. RE 40014 E1.
The known eye trackers provide time-resolved position data of the head and of the eyes of a test person/subject. There can be determined from these data, the time-resolved intersections of the line of sight of the viewer with the objects of static or dynamic scene, such as those arising when looking at a poster, an advert, the control panel of a machine (e.g. car or airplane cockpit), the display of a computer, etc. For this purpose, the position data of the head and the eyes of the viewer are determined at a relatively high sampling rate or frequency of up to 2000 Hz or more.
For the recording of data, there are usually various techniques available. For a wide range of applications, the most suitable technique, as being minimally invasive and therefore not burdensome to the subject or restrictive, is the video-based recording of the head of the subject at two different angles in the range of infrared light and a subsequent image processing, calculating from the image data in combination with geometry data of the experimental setting the position data of the head and the eyes.
From the image analysis, after a prior calibration, the head position and iris/pupil positions can be determined. This determination is often carried out in real-time and is performed by software being supplied by the manufacturer of the eye tracker. The data having this format, however, cannot be interpreted or understood by an end user, such as a psychologist who studies the scheme or visual behavior of watching a commercial. Therefore there is a need to process the data in an end-user friendly representation.
Usually the classification and interpretation of the data is performed by a decomposition of the recorded time series into fixations (cluster of viewpoints) and ballistic movements/jumps (saccades), where from a (cognitive) psychological perspective the fixations are of interest and the saccades are interesting in respect of the basic physiological processes. However, the computational methods and algorithms which are well-known for this purpose, are based on an evaluation of the viewpoints according to their spatial dispersion and/or to their velocity or acceleration. The algorithms require a parameterization done by the end user or default settings for the parameters done by the manufacturer which cover a wide application range, thereby becoming too imprecise for being used for specific applications, since the choice of parameterization can significantly affect the results of the algorithm. Further, the parameters are partly subjected to individual variations, referring to the subject or to a group of subjects, and/or are dependent on the tasks which are given for running the experiment. Therefore, in the known calculation methods and algorithms, the end user must have experience in the choice of parameters, which in practice is rarely the case, or he must rely on an approximate parameterization (“rule of thumb”). The known calculation methods or algorithms are not designed for the analysis of dynamic scenes where the viewer follows moving objects with his eyes. The separation of another kind of eye movement, the so-called smooth pursuit, as it occurs when watching dynamic scenes, is currently regarded as being difficult and has not yet been satisfactorily solved.